Efficient and Ad Hoc Estimation in the Bivariate Censoring Model

  • Mark J. van der Laan
Chapter

Abstract

A large number of proposals for estimating the bivariate survival function under random censoring has been made. In this paper we discuss nonparametric maximum likelihood estimation and the bivariate Kaplan-Meier estimator of Dabrowska. We show how these estimators are computed, present their intuitive background and compare their practical performance under different levels of dependence and censoring, based on extensive simulation results, which leads to a practical advise.

Keywords

Practical Performance Interval Censoring Lattice Partition Random Censoring Extensive Simulation Result 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media Dordrecht 1996

Authors and Affiliations

  • Mark J. van der Laan
    • 1
  1. 1.Division of Biostatistics School of Public HealthUniversity of CaliforniaBerkeleyUSA

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